Search Results (54)

Oases are the core carriers of societal development in arid regions, and their spatial patterns have changed significantly, driven by climate change and anthropogenic activities. This study integrates historical documents, archeological materials, maps, and remote sensing data. The changes in the temperature, precipitation, settlements, war frequency, and oasis area were identified by combining quantitative and qualitative methods, and the partial least squares path model (PLS-PM) was utilized to quantify the natural and human driving factors. The results show that the oasis development in the Tarim and Heihe River Basins exhibits distinct spatio-temporal variability and phased characteristics and is comprehensively shaped by both natural and anthropogenic drivers. The Tarim Basin’s natural oases demonstrate a “fluctuating recovery” pattern. The cultivated oases gradually expanded. The natural oases within the Heihe River Basin have persistently decreased, and cultivated oases show a “U”-shaped evolution pattern. This reflects the strong intervention of human reclamation in the cultivated oases. The introverted social ecosystem has endowed the Tarim River Basin with the ability to self-repair and achieve a periodic recovery. The Heihe River Basin serves as a strategic corridor for national external engagement, relying on regime stability. A regime collapse led to its lack of a stable recovery period. The PLS-PM reveals that the Tarim River Basin oasis evolution is predominantly driven by climate fluctuations. The path coefficient of natural factors for artificial oases is 0.63, and extreme drought leads to natural oasis contraction. The human influence dominates the Heihe River Basin, with a −0.93 path coefficient linking the cultivated oasis area to human factors. The frequency of wars (load 0.74) and changes in settlements (load −0.92) are the key factors. This study provides a powerful case for the analysis of the evolution and driving mechanism of future oases in drylands. Full article

With the growing global focus on sustainable development and climate change mitigation, promoting the low carbonization of energy systems has become an inevitable trend. Power load monitoring is crucial to achieving efficient power management, and load identification is the key link. The traditional load identification method has the problem of low accuracy. It is assumed that the technique of fusing harmonic features through color coding can improve the accuracy of load identification. In this paper, the load’s instantaneous reactive power, power factor and current sequence distribution characteristics are used as the mapping characteristics of the R, G and B channels of the two-dimensional V–I trajectory color image of the load using color coding technology. The harmonic amplitude characteristics are integrated to construct the mixed-color image of the load. The void residual shrinkage neural network is selected as the classification training model. The advantages and disadvantages of two residual shrinkage construction units, RSBU-CS and RSBU-CW, are analyzed. A single-load identification model with three RSBU-CWs is built. Different datasets verify the performance of the model. Compared with the test results of the ordinary color image dataset, the accuracy of the mixed-color image dataset is above 98%, and the accuracy of load identification is improved. Full article

Moisture ingress is a critical concern in buildings, as it may profoundly affect structural integrity, the energy efficiency of a building, and as well the quality of the indoor environment that, in turn, could influence the health and safety of building occupants. Moisture ingress can occur during any phase in the lifecycle of a building component, where environmental loads, such as precipitation, wind, snow, and elevated relative humidity, play a fundamental role in affecting the building structure. Climate change exacerbates the issue of moisture ingress by intensifying these loads. In this review paper, the statistical perspective on publications related to moisture ingress in building envelope materials (BEMs) was first assessed through a scientometric study. All relevant publications were gathered and manually filtered, and the selected papers were categorized based on the topics discussed. The results of the scientometric study, as presented in this paper, include a bar chart in which the number of publications in each category is illustrated; a science journal mapping diagram showing the interdisciplinary connections of the research; a cluster map depicting the network between topics; and an R&D momentum analysis reflecting the rate of growth and publication count in this field. Given the strong focus on material properties, this review also examines experimental methods for characterizing moisture transport properties in building materials used in BEMs. Additionally, the differences between various codes and standards centered on this topic are reviewed and discussed. This combined strategy is intended to comprehensively evaluate available information and approaches to permit identifying the knowledge gaps that need to be addressed. Full article

Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potential, service function, regenerative capacity, pasture productivity, and changes in plant species composition for five pilot regions in Kazakhstan. The current stages of these efforts are presented in this project report. The pasture lands in five regions, including Pavlodar (8,340,064 ha), North Kazakhstan (2,871,248 ha), Akmola (5,783,503 ha), Kostanay (11,762,318 ha), Karaganda (19,709,128 ha), and Ulytau (18,260,865 ha), were evaluated. Combined RS data were processed and the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Vegetation Cover (FCover), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC) indices were determined, in relation to the herbage of pastures and their growth and development, for field biophysical analysis. The highest values of LAI, FCOVER, and FARAR were recorded in the Akmola region, with index values of 18.5, 126.42, and 53.9, and the North Kazakhstan region, with index values of 17.89, 143.45, and 57.91, respectively. The massive 2024 spring floods, which occurred in the Akmola, North Kazakhstan, Kostanay, and Karaganda regions, caused many problems, particularly to civil constructions and buildings; however, these same floods had a very positive impact on pasture areas as they increased soil moisture. Further detailed investigations are ongoing to update the flood zones, wetlands, and swamp areas. The mapping of proper flood zones is required in Kazakhstan for pasture activities, rather than civil building construction. The related sustainable permissible grazing husbandry pasture loads are required to develop also. Recommendations for these preparation efforts are in the works. Full article

Seagrass meadows, including those formed by Zostera noltei, play a crucial role in marine ecosystem health by providing habitat stability and coastal protection. In the Romanian Black Sea, Z. noltei meadows are critically endangered due to pressures from eutrophication, habitat loss, and climate change. This study presents a comprehensive baseline assessment of Z. noltei meadows near Mangalia, Romania, utilizing in situ field methods and UAV mapping conducted in the spring and summer of 2023. Seven meadow sites (Z1–Z7) were identified, with notable variability in density, shoot counts, and coverage across sites. Site Z1 exhibited the highest density (1223 shoots/m⁻²) and Z5 and Z7 the longest leaves (an average of 60 cm), reflecting possible environmental influences. Statistical analyses revealed significant inter-site differences in shoot density and leaf length, with density emerging as a primary differentiator. Ex situ analyses of epiphyte load indicated a median, balanced epiphyte load. This baseline dataset supported the selection of Z1 as a reference donor site for seagrass relocation activities along the Romanian coast in 2023. By providing critical insights into Z. noltei structure and health, this study supports future conservation efforts and evidence-based management of these vulnerable coastal habitats. Full article

This study investigates soil loss in the Wadi Bin Abdullah watershed using the Revised Universal Soil Loss Equation (RUSLE) combined with advanced tools, such as remote sensing and the Geographic Information System (GIS). By leveraging the ALOS PALSAR Digital Elevation Model (DEM), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and the Digital Soil Map of the World (DSMW), the research accurately evaluates soil loss loads. The methodology identifies significant variations in soil loss rates across the entire watershed, with values ranging from 1 to 1189 tons per hectare per year. The classification of soil loss into four stages—very low (0–15 t/ha/yr), low (15–45 t/ha/yr), moderate (45–75 t/ha/yr), and high (>75 t/ha/yr)—provides a nuanced perspective on soil loss dynamics. Notably, 20% of the basin exhibited a soil loss rate of 36 tons per hectare per year. These high rates of soil erosion are attributed to certain factors, such as steep slopes, sparse vegetation cover, and intense rainfall events. These results align with regional and global studies and highlight the impact of topography, land use, and soil properties on soil loss. Moreover, the research emphasizes the importance of integrating empirical soil loss models with modern technological approaches to identify soil loss-prone locations and precisely quantify soil loss rates. These findings provide valuable insights for developing environmental management strategies aimed at mitigating the impacts of soil loss, promoting sustainable land use practices, and supporting resource conservation efforts in arid and semi-arid regions. Full article

Compatible model systems were developed for estimating fuel load dynamics in Ulex europaeus (gorse) and in Erica australis (Spanish heath) dominated shrub communities at stand level. The models were based on intensive, detailed destructive field sampling and were fitted simultaneously to fulfill the additivity principle. The models enable, for the first time, estimation of the biomass dynamics of the total shrub layer, size fractions and vegetative stage, with reasonably good accuracy. The approach used addresses the high variability in shrub biomass estimates by using a site index (SI) based on biomass levels at a reference age of 10 years. Analysis of the effect of climatic variables on site index confirmed the preference of gorse for mild temperatures and the ability of high heath communities to tolerate a wider range of temperatures. In the gorse communities, SI tended to increase as summer rainfall and the mean temperature of the coldest month increased. However, in the heath communities, no relationships were observed between SI and any of the climatic variables analyzed. The study findings may be useful for assessing and monitoring fuel hazards, updating fuel mapping, planning and implementing fuel reduction treatments and predicting fire behavior, among other important ecological and biomass use-related applications. Full article

Solar photovoltaic (SPV) power penetration in dispersed generation systems is constantly rising. Due to the elevated SPV penetration causing a lot of problems to power system stability, sustainability, reliable electricity production, and power quality, it is critical to forecast SPV power using climatic parameters. The suggested model is built with meteorological conditions as input parameters, and the effects of such variables on predicted SPV power have been studied. The primary goal of this study is to examine the effectiveness of optimization-based SPV power forecasting models based on meteorological conditions using the novel salp swarm algorithm due to its excellent ability for exploration and exploitation. To forecast SPV power, a recently designed approach that is based on the salp swarm algorithm (SSA) is used. The performance of the suggested optimization model is estimated in terms of statistical parameters which include Root Mean Square Error (RMSE), Mean Square Error (MSE), and Training Time (TT). To test the reliability and validity, the proposed algorithm is compared to grey wolf optimization (GWO) and the Levenberg–Marquardt-based artificial neural network algorithm. The values of RMSE and MSE obtained using the proposed SSA algorithm come out as 1.45% and 2.12% which are lesser when compared with other algorithms. Likewise, the TT for SSA is 12.46 s which is less than that of GWO by 8.15 s. The proposed model outperforms other intelligent techniques in terms of performance and robustness. The suggested method is applicable for load management operations in a microgrid environment. Moreover, the proposed study may serve as a road map for the Saudi government’s Vision 2030. Full article

Assessing interspecific adaptive genetic variation across environmental gradients offers insight into the scale of habitat-dependent heritable heterotic effects, which may ultimately enable pre-breeding for abiotic stress tolerance and novel climates. However, environmentally dependent allelic effects are often bypassed by intra-specific single-locality genome-wide associations studies (GWAS). Therefore, in order to bridge this gap, this study aimed at coupling an advanced panel of drought/heat susceptible common bean (Phaseolus vulgaris L.) × tolerant tepary bean (P. acutifolius A. Gray) interspecific lines with last-generation multi-environment GWAS algorithms to identify novel sources of heat and drought tolerance to the humid and dry subregions of the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme weather. A total of 87 advanced lines with interspecific ancestries were genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Five yield traits were recorded for each genotype and inputted in modern GWAS algorithms (i.e., FarmCPU and BLINK) to identify the putative associated loci across four localities in coastal Colombia. Best-fit models revealed 47 significant quantitative trait nucleotides (QTNs) distributed in all 11 common bean chromosomes. A total of 90 flanking candidate genes were identified using 1-kb genomic windows centered in each associated SNP marker. Pathway-enriched analyses were done using the mapped output of the GWAS for each yield trait. Some genes were directly linked to the drought tolerance response; morphological, physiological, and metabolic regulation; signal transduction; and fatty acid and phospholipid metabolism. We conclude that habitat-dependent interspecific polygenic effects are likely sufficient to boost common bean adaptation to the severe climate in coastal Colombia via introgression breeding. Environmental-dependent polygenic adaptation may be due to contrasting levels of selection and the deleterious load across localities. This work offers putative associated loci for marker-assisted and genomic selection targeting the common bean’s neo-tropical lowland adaptation to drought and heat. Full article

The path to European climate neutrality by 2050 will require comprehensive changes in all areas of life. For large industries such as steelworks, this results in the need for climate-friendly technologies. However, the age structure of existing steelworks makes transitional solutions such as carbon capture, utilization and storage (CCUS) necessary as short-term measures. Hence, a purposeful option is the integration of technical syntheses such as methanation into the overall process. This work summarizes hydrogen-intensified methanation experiments with synthetic steel gases in the novel additively manufactured reactor ‘ADDmeth1’. The studies include steady-state operating points at various reactor loads. Blast furnace gas (BFG), basic oxygen furnace gas (BOFG) and three mixtures of these two gases serve as carbon sources. The methanation achieved methane yields of 93.5% for BFG and 95.0% for BOFG in the one-stage once-through setup. The results suggest a kinetic limitation in the case of BFG methanation, while an equilibrium limitation is likely for BOFG. There is a smooth transition in all respects between the two extreme cases. The reaction channel inlet temperature ${\vartheta }_{in}$ showed a large influence on the reactor ignition behavior. By falling below the threshold value, a blow-off occurred during experimental operation. By means of a simulation model, practical operating maps were created which characterize permissible operating ranges for ${\vartheta }_{in}$ as a function of the gas composition and the reactor load. Full article

Jazan province on Saudi Arabia’s southwesterly Red Sea coast is facing significant challenges in water management related to its arid climate, restricted water resources, and increasing population. A total of 180 groundwater samples were collected and tested for important hydro-chemical parameters used to determine its adaptability for irrigation. The principal components analysis (PCA) was applied to evaluate the consistency/cluster overlapping, agglomeration in the datasets, and to identify the sources of variation between the 11 major ion concentrations (pH, K⁺, Na⁺, Mg²⁺, Ca²⁺, SO₄²⁻, Cl⁻, HCO₃⁻, NO₃⁻, TDS, and TH). The EC values ranged from excellent to unsuitable, with 10% being excellent to good, 43% permissible, and 47% improper for irrigation. The SAR classification determined that 91.67% of groundwater samples were good to excellent for irrigation, indicating that they are suitable for irrigation with no sodium-related adverse effects. Magnesium hazard values showed that 1.67% of the samples are unsuitable for irrigation, while the remaining 98.33% are suitable. Chloro-alkaline indices signify that most groundwater samples show positive ratios indicating that ion exchange is dominant in the aquifer. The Gibb’s diagram reflects that evaporation, seawater interaction, and water–rock interaction are the foremost processes impacting groundwater quality, besides other regional environmental variables. A strong positive correlation was declared between TDS and Na⁺, Mg²⁺, Ca²⁺, Cl⁻, SO₄²⁻ in addition to TH with Mg²⁺, Ca²⁺, Cl⁻, SO₄²⁻, TDS, and also Cl⁻ with Na⁺, Ca²⁺, Mg²⁺ were major connections, with correlation coefficients over 0.8 and p < 0.0001. The extracted factor analysis observed that TH, Ca²⁺, TDS, Cl⁻, and Mg²⁺ have high positive factor loading in Factor 1, with around 52% of the total variance. This confirms the roles of evaporation and ion exchange as the major processes that mostly affect groundwater quality, along with very little human impact. The spatial distribution maps of the various water quality indices showed that the majority of unsuitable groundwater samples were falling along the coast where there is overcrowding and a variety of anthropogenic activities and the possible impact of seawater intrusion. The results of the hierarchical cluster analysis agreed with the correlations mentioned in the factor analysis and correlation matrix. As a result, incorporating physicochemical variables into the PCA to assess groundwater quality is a practical and adaptable approach with exceptional abilities and new perspectives. According to the study’s findings, incorporating different techniques to assess groundwater quality is beneficial in understanding the factors that control groundwater quality and can assist officials in effectively controlling groundwater quality and also enhancing the water resources in the study area. Full article

Climate change is a critical challenge for the global grape and wine industry, as it can disrupt grapevine growth, production, and wine quality. Climate change could influence the cost-effectiveness and growth of the wine industry in different wine regions since grapevine development is deeply dependent on weather (short-term) and climate (long-term) conditions. Innovation and new technologies are needed to meet the challenge. This review article addresses the impact of climate change on grapevines, such as vine phenology, pest and disease pressure, crop load, and grape and wine composition. It also reviews recent advances in the areas of viticultural manipulation and relevant technologies to potentially reduce the impact of climate change and help growers improve grape quality. Remote sensing is used for vineyard microclimate monitoring; thermal sensors combined with UAVs, aircraft, or satellites are used for water management; soil electrical conductivity sensors have been developed for soil mapping. Viticultural manipulations, such as regulated deficit irrigation for water use efficiency and berry-ripening delay for growing quality fruit, are also discussed. The review assesses future directions for further technological development, such as soil and vine water monitoring devises, precision viticulture, and artificial intelligence in vineyards. Full article

This study examines the sediment retention in Wadi Baysh using the Revised Universal Soil Loss Equation (RUSLE) and TerrSet models, accompanied by integrated remote sensing and Geographic Information System (GIS) techniques. The contribution of this study is mainly associated with the application of TerrSet integrated with high resolution datasets to precisely estimate sediments load, which provide useful information to operate dams and improve the operational efficiency of dams. The Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data are utilized to delineate the basin and have been used as an input to the TerrSet model. The rainfall erosivity (R factor) was calculated using the Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) in the research area during 2015–2020. The soil erodibility (K factor) and LULC categorization are calculated using the digital soil map of the world (DSMW) and Sentinel-2 datasets, respectively. The R factor calculated for Wadi Baysh ranges between 91.35 and 115.95 MJ mm/ha/h/year, while the estimated K factor ranges from 0.139 to 0.151 t ha h/ha M. The Support Vector Machine (SVM) method categorized LULC of the study area into four major classes including barren land (81% of the total area), built-up area (11%), vegetation (8%), and water bodies (1%). Results from the sediment retention module (TerrSet) indicated that each year, 57.91 million tons of soil loss occurred in the basin. The data show that soil loss is greater in the northeast and south, whereas it is typical in the middle of Wadi Baysh. It is concluded from the current analyses that the dam lake of Wadi Baysh, located downstream, will be filled soon in the coming few years if sediment loads are carried to the lake at the same rate. Surface dam operators can obtain a full understanding of sedimentation and take proactive measures to reduce its influence on dam operations by leveraging TerrSet’s sophisticated capabilities. Full article

The vast Angara region, with an area of 13.8 million ha, is located in the southern taiga of central Siberia, Russia. This is one of the most disturbed regions by both fire and logging in northern Asia. We have developed surface and ground fuel-load maps by integrating satellite and ground-based data with respect to the forest-growing conditions and the disturbance of the territory by anthropogenic and natural factors (fires and logging). We found that from 2001 to 2020, fuel loads increased by 8% in the study region, mainly due to a large amount of down woody debris at clearcuts and burned sites. The expansion of the disturbed areas in the Angara region resulted in an increase in natural fire hazards in spring and summer. Annual carbon emissions from fires varied from 0.06 to 6.18 Mt, with summer emissions accounting for more than 95% in extreme fire years and 31–68% in the years of low fire activity. While the trend in the increase in annual carbon emissions from fires is not statistically significant due to its high interannual variability and a large disturbance of the study area, there are significantly increasing trends in mean carbon emissions from fires per unit area (p < 0.005) and decadal means (p < 0.1). In addition, we found significant trends in the increase in emissions released by severe fires (p < 0.005) and by fires in wetter, dark, coniferous (spruce, p < 0.005 and Siberian pine, p < 0.025) forests. This indicates deeper burning and loss of legacy carbon that impacts on the carbon cycle resulting in climate feedback. Full article

Low-sloped roofs such as commercial and high-rise buildings in a dense urban area provide vast “unused” surfaces that can be used to manage storm-water and mitigate urban flooding. Storm-water Detention Assembly (SDA) known as “blue roof” exerts advanced drainage technologies and operational strategies to store-and-release storm-water during and after heavy rainfall events. SDA can reduce peak flow and decrease storm-water run-off volume. However, the SDA application is limited due to the lack of science-based regulatory requirements. This paper introduces SDA, summarizes the benefits and challenges of this system, reviews the existing code specifications on roofing drain systems, and documents the criteria to design and construct SDA. With this systematic approach, the missing links are identified that shall form code change request for possible inclusion in the National Building Code of Canada (NBCC) and National Plumping Code of Canada (NPC). In addition, a road map is stated to calculate and benchmark the drain requirements on low-sloped roofs for a given design climatic load. Full article